International Journal of
Environmental Research and Public Health Article
Prevalence and Associated Factors of Secondhand Smoke Exposure among Internal Chinese Migrant Women of Reproductive Age: Evidence from China’s Labor-Force Dynamic Survey Xiao Gong 1,2 , Xiaofeng Luo 1,2 and Li Ling 1,2, * 1 2
*
Faculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China;
[email protected] (X.G.);
[email protected] (X.L.) Sun Yat-sen Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou 510080, China Correspondence:
[email protected]; Tel.: +86-020-8733-5524; Fax: +86-020-8733-5524
Academic Editor: Konstantinos Farsalinos Received: 24 January 2016; Accepted: 23 March 2016; Published: 1 April 2016
Abstract: Secondhand smoke (SHS) is a major risk factor for poor health outcomes among women in China, where proportionately few women smoke. This is especially the case as it pertains to women’s reproductive health, specifically migrant women who are exposed to SHS more than the population at large. There are several factors which may increase migrant women’s risk of SHS exposure. This paper aims to investigate the prevalence and associated factors of SHS exposure among internal Chinese migrant women of reproductive age. The data used were derived from the 2014 Chinese Labor Dynamic Survey, a national representative panel survey. The age-adjusted rate of SHS exposure of women of reproductive age with migration experience was of 43.46% (95% CI: 40.73%–46.40%), higher than those without migration experience (35.28% (95% CI: 33.66%–36.97%)). Multivariate analysis showed that participants with a marital status of “Widowed” had statistically lower exposure rates, while those with a status of “Cohabitation” had statistically higher exposure. Those with an undergraduate degree or above had statistically lower SHS exposure. Those with increasing levels of social support, and those who currently smoke or drink alcohol, had statistically higher SHS exposure. Participants’ different work-places had an effect on their SHS exposure, with outdoor workers statistically more exposed. Our findings suggest that urgent tobacco control measures should be taken to reduce smoking prevalence and SHS exposure. Specific attention should be paid to protecting migrant women of reproductive age from SHS. Keywords: internal migration; secondhand smoke; women of reproductive age; China
1. Introduction Tobacco use is a worldwide public health problem, and is a major preventable risk factor of diseases and death [1]. Estimated total tobacco-attributable deaths will reach 8.3 million in 2030, mainly from low- and middle-income countries [2]. Secondhand smoke (SHS) is almost as risky as smoking itself. SHS not only impairs the health of those exposed [3–5], but also leads to adverse reproductive outcomes for exposed reproductive women, such as infertility [6], spontaneous abortion [7], low birthweight [8], preterm birth [9–11], infant death [12], and is also dangerous to the health of children [13,14]. China has a high tobacco prevalence and high levels of secondhand smoke exposure, which results in a particularly high smoking-related disease burden that is still on the rise [15,16]. As reported by the Global Adult Tobacco Survey (GATS) [17], in 2010, an estimated 28.1% of adults in China (52.9% of men and 2.4% of women) were smokers. Women, although with a relatively low smoking rate, Int. J. Environ. Res. Public Health 2016, 13, 371; doi:10.3390/ijerph13040371
www.mdpi.com/journal/ijerph
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are exposed to tremendous hazards of the high tobacco prevalence, bearing nearly 80% of the total disease burden of SHS [18]. As reported by nonsmokers, 72.4% were exposed to secondhand smoke with 38.0% exposed on a daily basis [19]. However, most Chinese women do not fully recognize these hazards and therefore do not successfully avoid SHS exposure [17], even before and/or during their pregnancy [20]. The Chinese population has experienced a massive internal migration tide since the economic reform and opening-up. The number of migrants leaving their officially registered places of residence has been estimated as 50 million in 1990, 121 million in 2000, and 253 million in 2014 [21]. Of these 253 million 78% are between the ages of 15 to 59, and half of them are women [21]. Due to the Chinese hukou (household registry) system, these migrants do not have the same rights as the local permanent residents. These rights include benefits such as social security and welfare, which are tied to permanent residency registration. In addition to those institutional reasons, leaving their previous residences and living a migratory life, is in itself a hazardous situation for those migrants. All of the above reasons make migrants a particularly vulnerable population. According to a series of studies of different groups of migrants, migrants have higher smoking rates than the general population, ranging from 48.7% to 64.9% for males, and from 2.1% to 10.9% for females [22–26]. Although we believe migrants have a high tobacco contact rate, studies aiming to investigate the SHS exposure of the migrant population, especially migrant reproductive women are still lacking. Some characteristics of reproductive migrant women, such as younger average age, low education levels, and migratory lifestyles as well as their work and living environments [5,21,27] may increase their exposure to SHS. Several studies have reported that SHS exposure among migrant women is between 62% and 64% [28,29]. These estimates were based on subgroups of migrants (including several typical employed populations) and are not representative of the entire migrant population. Additionally, the definition of SHS exposure in those studies did not consider the exposure time duration, but only tested whether there was any chance of exposure to SHS. This loose definition weakens the explanatory power of these studies on the health impact estimate of SHS exposure. Our study uses a nationally representative sample to estimate the SHS exposure (defined as more than 15 min daily exposure) rate among internal Chinese migrant women of reproductive age. It also makes an age-adjusted rates comparison to those without migration experience, and investigates associated factors which may impact the exposure. 2. Methods 2.1. Data and Sampling The data were derived from the 2014 Chinese Labor Dynamic Survey (CLDS), which was the second wave of a nationally representative panel survey [30]. The survey was sponsored by the Center for Social Survey at Sen Yat-sen University. It used computer assisted personal interviewing methodology and sampled with a multi-stage stratified sampling method. The survey targeted individuals age 15 to 65, and covered 29 out of 31 province-level administrative areas of mainland China (excluding Tibet and Hainan). The second wave was conducted in the same communities as the first wave (applied in year 2012). Of a total of 23,594 respondents in the second wave, 10,053 were follow up samples from the first wave, and 13,541 were newly added participants. We extracted and analyzed the data from women aged 15 to 49, the range usually used as reproductive age in World Health Organization (WHO) documents [31]. 2.2. Variable Definitions 2.2.1. Secondhand Smoke SHS exposure has been defined as exposure to another person’s tobacco smoke for at least 15 min per day for more than one day every week [32]. Participants were asked “the average frequency they
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were exposed to another person’s tobacco smoke for at least 15 min per day”, with choices of “Almost every day”, “Equal to or more than 3 days per week”, “Equal to or more than 1 day and less than 3 days per week”, “Less than 1 day per week” and “No exposure”. Participants who chose one of the first three choices were recognized as SHS exposed. 2.2.2. Migration Experience In this study, we categorize the overall population into two groups, those with migration experience and those without migration experience. We define people with migration experience as those who had the experience of leaving their registered residence (according to the hukou system) for more than 6 months. This “6-months” definition has been widely used by the Chinese government as a handy management rule. In the Chinese academic community, researchers have also accepted this definition as the migratory lifestyle may require a period to affect the behavior or health of those migrants. 2.2.3. Demographic Variates Demographic characteristics used in this paper include age group (“15–19”, “20–29”, ”30–39”, ”40–49”), marital status (“Single”, “Married: First Marriage”, “Married: Non-first Marriage”, “Divorced” and “Widowed”), and education level (“Primary School or Illiteracy”, “Secondary School”, “High School or Equivalent” and “Undergraduate or Above”). 2.2.4. Other Covariates The social support variable was first a score of the linear combination of three questions examining the numbers of people that the participant could refer to when they needed to speak their minds, discuss important issues or borrow money equivalent to 5000 RMB (approximately 800 US dollars). Then we divided it into four levels according to the score’s distribution characteristics, which were named “No Support”, “Low Support”, “Moderate Support”, and “High Support”. Self-reported health status was a Likert 5-scale item, with “Excellent”, “Good”, “Fair”, “Poor” and “Very Poor” as the five scale answers. Tobacco smoke and alcohol use status were also recorded by the CLDS. Participants were categorized into “Smokers” and “Non-smokers” according to whether they had smoked in the past 30 days. Since women generally drink less alcohol in China, we categorized them into two levels according to the data distribution: “Less than once a week” and “Once a week or more”. Work place information was also included in our study, and recoded as “Outdoors”, “Factory Workshop”, “Indoor Business Places”, “Office”, “Home (working from home)” and “Unfixed or Other (including those workers with unfixed workplaces and those without jobs)”. 2.3. Statistical Analysis The statistical software R (Version 3.2.0. R Foundation for Statistical Computing, Vienna, Austria) was used for statistical analysis. To make better comparisons of SHS exposure between different groups with different age structures, standardized rates were calculated, and a direct method was used [33,34]. Univariate and multivariate logistic regressions were performed to identify the factors associated with SHS exposure. p-values of less than 0.05 were considered statistically significant. 3. Results 3.1. Demographics of the Study Population We had 7617 women of reproductive age identified out of all survey participants (Table 1). Of those participants, 41.1% were between 40 and 49; the majority were married and on their first marriage (76.6%). Most of the participants were not well educated (29.7% were primary school or below, and 34.4% were secondary school).
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Table 1. Demographic characteristics of participants by migration experience and secondhand smoke status.
Variables Sample Size Age Group 15–19 20–29 30–39 40–49 Marital Status Single Married: First Marriage Married: Non-first Marriage Divorced Widowed Cohabitation Education Level Primary School or Below Secondary School High School or Equivalent Undergraduate or Above Social Support No Support Low Support Moderate Support High Support Self-reported Health Status Excellent Good Fair Poor Very Poor Tobacco Use Non-smokers Smokers Alcohol Use Less than once a week Once a week or more Work Place Outdoors Factory Workshop Indoor Business Places Office Home Unfixed or Other
Number
Participants With Migration Experience
Participants Without Migration Experience
SHS (N, %)
Non-SHS (N, %)
SHS (N, %)
Non-SHS (N, %)
7617
1094
1398
1806
3319
732 (9.6) 1754 (23.0) 1998 (26.2) 3133 (41.1)
29 (2.7) 308 (28.2) 355 (32.4) 402 (36.7)
48 (3.4) 438 (31.3) 418 (29.9) 494 (35.3)
214 (11.8) 340 (18.8) 437 (24.2) 815 (45.1)
441 (13.3) 668 (20.1) 788 (23.7) 1422 (42.8)
1425 (18.7) 5833 (76.6) 141 (1.9) 82 (1.1) 62 (0.8) 72 (0.9)
99 (9.0) 944 (86.3) 23 (2.1) 9 (0.8) 4 (0.4) 15 (1.4)
185 (13.2) 1146 (82) 21 (1.5) 17 (1.2) 21 (1.5) 8 (0.6)
362 (20.1) 1380 (76.5) 25 (1.4) 14 (0.8) 10 (0.6) 14 (0.8)
779 (23.5) 2363 (71.2) 72 (2.2) 42 (1.3) 27 (0.8) 35 (1.1)
2263 (29.7) 2618 (34.4) 1355 (17.8) 1381 (18.1)
325 (29.7) 429 (39.2) 177 (16.2) 163 (14.9)
366 (26.2) 493 (35.3) 229 (16.4) 310 (22.2)
568 (31.5) 611 (33.8) 331 (18.3) 296 (16.4)
1004 (30.3) 1085 (32.7) 618 (18.6) 612 (18.4)
1022 (13.4) 1508 (19.8) 2789 (36.6) 2298 (30.2)
144 (13.2) 256 (23.4) 403 (36.8) 291 (26.6)
242 (17.3) 330 (23.6) 479 (34.3) 347 (24.8)
208 (11.5) 346 (19.2) 694 (38.4) 558 (30.9)
428 (12.9) 576 (17.4) 1213 (36.5) 1102 (33.2)
1925 (25.3) 3328 (43.7) 1750 (23) 540 (7.1) 74 (1.0)
231 (21.1) 462 (42.2) 303 (27.7) 87 (8.0) 11 (1.0)
322 (23) 600 (42.9) 379 (27.1) 88 (6.3) 9 (0.6)
429 (23.8) 792 (43.9) 397 (22) 169 (9.4) 19 (1.1)
943 (28.4) 1474 (44.4) 671 (20.2) 196 (5.9) 35 (1.1)
7524 (98.8) 93 (1.2)
1072 (98.0) 22 (2.0)
1390 (99.4) 8 (0.6)
1767 (97.8) 39 (2.2)
3295 (99.3) 24 (0.7)
7437 (97.6) 180 (2.4)
1052 (96.2) 42 (3.8)
1373 (98.2) 25 (1.8)
1747 (96.7) 59 (3.3)
3265 (98.4) 54 (1.6)
1723 (22.6) 690 (9.1) 1020 (13.4) 1190 (15.6) 294 (3.9) 2700 (35.4)
236 (21.6) 156 (14.3) 194 (17.7) 188 (17.2) 42 (3.8) 278 (25.4)
213 (15.2) 186 (13.3) 272 (19.5) 271 (19.4) 71 (5.1) 385 (27.5)
473 (26.2) 138 (7.6) 206 (11.4) 293 (16.2) 71 (3.9) 625 (34.6)
801 (24.1) 210 (6.3) 348 (10.5) 438 (13.2) 110 (3.3) 1412 (42.5)
3.2. SHS Exposure Status by Migration Experience Of the 7617 participants, 2492 had migration experience and 5125 did not. Among participants with migration experience, 1094 (43.9%) had been exposed to SHS. 1806 (35.2%) of those without migration experience had been exposed (Table 1). Figure 1 demonstrates the SHS exposure rates of different age groups (“15–19”, “20–24”, “25–29”, “30–34”, “35–39”, “40–44”, “45–49”) for all participants, participants with and participants without migration experience. It shows that SHS exposure rates among women with migration experience were higher than among those without migration experience. SHS exposure rates also showed disparities between different age groups (Figure 1). The migrant population has an age structure different from the non-migrant population; migrants, on average, are younger [21]. Since age may be a confounding factor for the SHS exposure rates estimation and comparison, we calculated both the crude and the age-adjusted rates, as well as a 95% confidence interval.
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Figure 1. 1. Secondhand rates of of different different age age groups groups for for all all participants. participants. Figure Secondhand smoking smoking rates
For individuals with migration experience, the crude SHS exposure rate was 43.90%, with an For individuals with migration the crude SHS exposure rate migration was 43.90%, with an age-adjusted rate of 43.46% (95% CI: experience, 40.73%–46.40%). For individuals without experience, age-adjusted of 43.46% (95%35.24%, CI: 40.73%–46.40%). For individuals without experience, the crude SHSrate exposure rate was with an age-adjusted rate of 35.28% (95%migration CI: 33.66%–36.97%). the crude SHS exposure rate was 35.24%, with an age-adjusted rate of 35.28% (95% CI: 33.66%–36.97%). 3.3. Associated Factors of SHS Exposure: Results of Univariate and Multivariate Logistic Regressions 3.3. Associated Factors of SHS Exposure: Results of Univariate and Multivariate Logistic Regressions We tested the associated factors of SHS exposure among migrant women of reproductive age We tested the associated factors of SHS exposure among migrant women of reproductive age through univariate and multivariate logistic regression (Table 2). through univariate and multivariate logistic regression (Table 2). Table 2. Associated factors of secondhand smoke among migrant women of reproductive age through Table 2. Associated factors of secondhand smoke among migrant women of reproductive age through univariate and multivariate logistic regression (N = 2492). univariate and multivariate logistic regression (N = 2492). Univariate Logistic Variables Variables
Age Group Age Group 15–19 20–2915–19 30–39 40–4920–29 Marital30–39 Status Single 40–49First Marriage Married: Married: MaritalNon–first Status Marriage Single Divorced Widowed Married: First Marriage Cohabitation Married: Non–first Marriage Education Level Primary School or Below Divorced Secondary School Widowed High School or Equivalent Undergraduate Cohabitationor Above
Univariate Logistic Regression Regression
Multivariate Logistic
Multivariate Logistic Regression Regression
OR (95% CI)
p–Value
Reference 1.16 (0.72–1.91) Reference 1.41 (0.87–2.30) (0.72–1.91) 1.351.16 (0.84–2.20)
0.538 0.166 0.538 0.223
Reference 1.18 (0.69–2.04) Reference 1.21 (0.68–2.18) 1.18 1.07(0.69–2.04) (0.60–1.93)
0.554 0.512 0.5540.827
1.41 (0.87–2.30)
OR (95% CI)
p–Value
OR (95% CI)
OR (95% CI)
0.166
1.21 (0.68–2.18)
0.512
Reference (0.84–2.20) 1.541.35 (1.19–2.00)
0.223 0.001
Reference 1.07 1.24(0.60–1.93) (0.89–1.73)
0.8270.207
2.05 (1.08–3.91)
0.028
1.46 (0.72–2.95)
0.291 0.896
0.361.54 (0.10–0.97) (1.19–2.00) 3.50 (1.47–8.97)
0.98 0.065 0.001 0.006
Reference 0.94 (0.37–2.25)
0.21(0.89–1.73) (0.06–0.62) 1.24
0.2070.009
0.028
1.46 (0.72–2.95)
0.291
Reference 0.99 (0.41–2.25) 0.98 (0.80–1.19) 0.36 (0.10–0.97) 0.87 (0.68–1.11) 0.593.50 (0.46–0.75) (1.47–8.97)
0.98
0.94 Reference (0.37–2.25)
0.896
0.841 0.065 0.27
